In the field of digital image processing, an original image, such as a photographic negative, is sampled periodically to produce a digital representation of the original image. The digital image is processed by applying image processing functions to improve such image qualities as sharpness and tone scale. The processed digital image is then displayed on output media such as a CRT or photographic film or paper.
FIG. 2 is a schematic diagram of a representative apparatus employing digital image processing. Such apparatus includes an input device 10 for sampling the original image and an analog-to-digital converter 12 for producing the digital representation of the original image. Commonly employed input devices include drum and flat bed scanners, linear and area solid state image sensing arrays, and CRT and laser flying spot scanners.
The digital image is stored in a mass memory 14, such as a solid state frame buffer, magnetic tape, or disc storage device. A digital computer 16 applies the various image processing functions to the digital image to produce the processed digital image.
The digital computer 16 may comprise, for example, a main frame general purpose digital computer, or for higher speed operation, a digital computer specially configured for high speed digital processing of images.
The processed digital image is converted to sampled analog form by a digital-to-analog converter 18. The processed digital image is displayed on an output device 20 such as a drum or flat bed graphic arts scanner, or a CRT or laser flying spot scanner. The elements of the image reproduction apparatus communicate via a data and control bus 22. As noted above, one of the processing functions performed by the digital computer is to adjust the tone scale of the processed image. There is a continuing effort in the field of digital image processing to automatically determine the optimum tone reproduction function employed by the digital computer.
The basic method of tone reproduction in digital image processing is shown graphically in FIG. 3. As shown in the upper left quadrant of the graph in FIG. 3, each input signal level (measured by the input device 10 in FIG. 1) is translated to an input tone value by an input calibration function, represented by the curve labeled 24. Each input tone value is converted to an output tone value by the tone reproduction function shown as the curve labeled 26 in the upper right quadrant of the graph. Finally, each output tone value is converted to an output device level by an output calibration function shown by the curve labeled 28 in the lower right quadrant of the graph.
The input and output calibration functions are determined by the physical characteristics of the input and output devices and the input and output media. The optimum tone reproduction function, on the other hand, depends upon the tonal characteristics of the original image, and preferably is custom tailored for each image that is reproduced. Investigators have searched for a scene invariant parameter that could be used to define an optimum tone reproduction function.
This effort led some investigators to hypothesize that the highly modulated (busy) parts of a high quality image follow a normal (Gaussian) frequency distribution with respect to tone values. See for example U.S.S.R. Inventor's Certificate No. 297976 (1971) entitled "Process for the Evaluation of the Image Quality" by Ovchinnikov et al. Ovchinnikov and his coworkers went on to demonstrate that the appearance of digitally processed photographic images could be improved by using a tone reproduction function that is generated by normalizing the distribution of a statistical sample of tone values (a lightness scale was employed) taken from parts of the image where the first derivative of lightness with respect to distance in the image was greater than some predetermined minimum threshold. See the article entitled "A New Approach to Programming in Photomechanical Reproduction" by Yu. Ovchinnikov et al. The l2th IARIGAI Conference Proc., Versailles, France, Ed. W. Banks, IPC Science and Technology Press, Guildford, England 1974, pp. 160-163.
Briefly, the method of Ovchinnikov et al. involves scanning the original image and randomly sampling the tone values (lightness) occuring in parts of the image where the first derivative of lightness is above some predetermined minimum threshold value. These sampled tone values are compiled in a histogram, illustrated by the curve labeled 30 in the lower right quadrant of FIG. 4. A normal distribution is shown as the curve labeled 32 in the upper left quadrant of FIG. 4. The method for generating the tone reproduction function involves constructing a function that transforms the sampled tone value distribution into the normal distribution. The optimum tone reproduction function for the whole image is then taken as that function. This tone reproduction function is shown as the curve labeled 34 in the upper right hand corner of FIG. 4. In this prior art method, the tone reproduction function relates each lightness value in the input to an output lightness value.
After the tone reproduction function is generated, it is applied to each tone value of the digital image to produce the processed digital image.
Although the digital image processing method proposed by Ovchinnikov et al. lends itself to substantial automation in a digital computer, there are times when the method will fail to produce optimum results. For example, when a scene contains a multitude of specular reflections (from waves on water for instance) the statistical sampling process will accumulate many samples from the edges of the specular reflections, causing the resulting tone reproduction function to be undesirably distorted.
The object of the present invention is to provide a digital image processing method employing a tone reproduction function generated by normalizing the distribution of a sample of tone values selected from the informational part of the image that is less affected by the problem noted above.